{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mixture Density Networks in GPflow\n",
    "\n",
    "In this tutorial, we'll implement a Mixture Density Network (MDN) [1] in GPflow. This post is very similar to a [blogpost](http://blog.otoro.net/2015/11/24/mixture-density-networks-with-tensorflow/) from 2015, but instead of directly using TensorFlow we'll use GPflow. GPflow is typically used for building Gaussian Process based models but the framework contains tons of useful methods and classes that can be used to quickly prototype a wide variety of ML algorithms. Excellent for doing research!\n",
    "\n",
    "This post is organized as follows. We start by giving a quick motivation of why MDNs can be useful. We then go over a GPflow implementation of the model and use it for a couple of toy experiments.\n",
    "\n",
    "## Conditional Density Estimation\n",
    "\n",
    "Imagine for a moment that we are interested in performing regression on the following dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "CMAP = plt.get_cmap('Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 200\n",
    "NOISE_STD = 5.0e-2\n",
    "\n",
    "def sinusoidal_data(N, noise):\n",
    "    Y = np.linspace(-2, 2, N)[:, None]\n",
    "    X = np.sin(4 * Y) * 2.0 + Y * 0.5\n",
    "    X += np.random.randn(N, 1) * noise\n",
    "    Y += np.random.randn(N, 1) * noise\n",
    "    return X, Y\n",
    "\n",
    "X, Y = sinusoidal_data(N, NOISE_STD)\n",
    "plt.plot(X, Y,'ro', alpha=0.3);\n",
    "plt.xlabel(\"$x$\");\n",
    "plt.ylabel(\"$y$\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first sight, this dataset doesn't look too scary, both input and output are single dimensional and the data has a clear sinusoidal pattern. However, we notice that a single input $x$ can correspond to multiple output values $y$. For example, for $x=0$ the output can be on of the values $\\{-1.5, -3/4, 0, 0.8, 1.5\\}$. Typical regression algorithms such as Linear Regression, Gaussian processes regression, Multilayer Perceptrons (MLPs), etc would struggle with this as they predict only one value for every input.\n",
    "\n",
    "To model a dataset like this we will use Conditional Density Estimation (CDE) models. CDE models deal with inferring $p(f(x)|x)$ instead of only calculating only the expectation $E[f(x) | x]$. Modeling the complete distribution $p(f(x)|x)$ is typically harder but it reveals much more interesting properties, such as the modes, outlier boundaries and samples. A real-world example where CDE is required is the modeling of taxi drop-offs conditioned on the pick-up location. We would expect a taxi drop-off location to be multi-modal as passengers need to go to different places (e.g. airport/city center/suburbs) and that the density depends on the starting location [2]. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mixture Density Models\n",
    "\n",
    "Mixture density networks (MDNs) are a parametric class of models that allow for conditional density estimation. They are built out of 2 parts: a neural net and a Mixture of Gaussians (MoG). The neural net is  responsible for producing the characteristics of the MoG. In practice, given that the MoG consists of $M$ Gaussians, the neural net will output a collection of $M$ means, variances and weights $\\{\\mu_m, \\sigma_m^2, \\pi_m\\}_{m=1}^M$. The produced means, variances and weights are used to define the conditional probability distribution function \n",
    "$$\n",
    "p(Y = y\\,|\\,X = x) = \\sum_{m=1}^{M} \\pi_{m}(x)\\,\\mathcal{N}\\big(y\\, \\left|\\,\\mu_{m}(x), \\sigma_{m}^2(x)\\big)\\right.\n",
    "$$\n",
    "Each of the parameters  $\\pi_{m}(x), \\mu_{m}(x), \\sigma_{m}(x)$ of the distribution will be determined by the neural network, as a function of the input $x$.\n",
    "\n",
    "We train the MDN's neural net by optimizing the model's likelihood\n",
    "$$\n",
    " \\mathcal{L} \\triangleq \\text{argmax}_{\\Theta} \\prod_{n=1}^N p(Y = y_n | X = x_n),\n",
    "$$\n",
    "where $\\Theta$ collects the neural nets weights and biases and $\\{x_n, y_n\\}_{n=1}^N$ are our training dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPflow MDN implementation\n",
    "\n",
    "GPflow doesn't reinvent the wheel: most of what you will see is just plain Python/TensorFlow code. We choose to use GPflow, however, because it provides us with some functionality to easily define a model. Once we have a GPflow model, we can specify its objective function, parameters, dataset. This extra layer of abstraction makes interacting with the model, such as optimizing or performing inference, much easier. \n",
    "\n",
    "We begin by importing the required packages from TensorFlow and GPflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gpflow\n",
    "from gpflow.models.model import Model\n",
    "from gpflow import DataHolder, Param, ParamList, params_as_tensors, autoflow, settings\n",
    "from gpflow.training import AdamOptimizer, ScipyOptimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We continue by creating a `MDN` class which inherits from GPflow's `model` class. We now need to do the following things:\n",
    "1. Store the feature and target matrices (X, Y) as `DataHolders`.\n",
    "2. Define our model's parameters using GPflow's `Parameter` and `ParamList` objects.\n",
    "3. Define the objective function. In the `_build_likelood` function we need to specify our model's objective function. When we optimize the model the negative of this function will be minimized. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MDN(Model):\n",
    "    \n",
    "    def __init__(self, X, Y, inner_dims=[10, 10,], activation=tf.nn.tanh, num_mixtures=5):\n",
    "        Model.__init__(self)\n",
    "        \n",
    "        self.Din = X.shape[1]\n",
    "        # `self.dims` collects the NNs input, hidden and output dimensions.\n",
    "        # the number of output dims, `self.dims[-1]` equals `num_mixtures` means +\n",
    "        # `num_mixtures` variances + `num_mixtures` weights, a total of\n",
    "        # 3 times `num_mixtures` variables.\n",
    "        self.dims = [self.Din, ] + list(inner_dims) + [3 * num_mixtures]\n",
    "        self.activation = activation\n",
    "\n",
    "        self.X = DataHolder(X)\n",
    "        self.Y = DataHolder(Y)\n",
    "\n",
    "        self._create_network()\n",
    "        \n",
    "    def _create_network(self):\n",
    "        Ws, bs = [], []\n",
    "        for dim_in, dim_out in zip(self.dims[:-1], self.dims[1:]):\n",
    "            init_xavier_std = (2.0 / (dim_in + dim_out)) ** 0.5\n",
    "            Ws.append(Param(np.random.randn(dim_in, dim_out) * init_xavier_std))\n",
    "            bs.append(Param(np.zeros(dim_out)))\n",
    "\n",
    "        self.Ws, self.bs = ParamList(Ws), ParamList(bs)\n",
    "\n",
    "    @params_as_tensors\n",
    "    def _eval_network(self, X):\n",
    "        for i, (W, b) in enumerate(zip(self.Ws, self.bs)):\n",
    "            X = tf.matmul(X, W) + b\n",
    "            if i < len(self.bs) - 1:\n",
    "                X = self.activation(X)\n",
    "\n",
    "        pis, mus, sigmas = tf.split(X, 3, axis=1)\n",
    "        pis = tf.nn.softmax(pis)  # make sure they normalize to 1\n",
    "        sigmas = tf.exp(sigmas)   # make sure std. dev. are positive\n",
    "        \n",
    "        return pis, mus, sigmas\n",
    "\n",
    "    @params_as_tensors\n",
    "    def _build_likelihood(self):\n",
    "        pis, mus, sigmas = self._eval_network(self.X)\n",
    "        Z = (2 * np.pi)**0.5 * sigmas\n",
    "        log_probs_mog = (-0.5 * (mus - self.Y)**2 / sigmas**2) - tf.log(Z) + tf.log(pis)\n",
    "        log_probs = tf.reduce_logsumexp(log_probs_mog, axis=1)\n",
    "        return tf.reduce_sum(log_probs)\n",
    "    \n",
    "    @autoflow((settings.float_type, [None, None]))\n",
    "    def eval_network(self, X):\n",
    "        pis, mus, sigmas = self._eval_network(X)\n",
    "        return pis, mus, sigmas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes\n",
    "- Given that we are dealing with a MoG, the neural net output must comply with the following restrictions:\n",
    "   $\\sum_{m=1}^{M} \\pi_{m}(x) = 1$, $\\pi_m \\ge 0$ and $\\sigma_m\\ \\forall\\ m$.\n",
    "    We achieve this by applying the `softmax` operator to the $\\pi$'s and by taking the `exp` to the $\\sigma$'s.\n",
    "\n",
    "- We use the \"Xavier\" initialization for the neural network's weights. (Glorot and Bengio, 2010).\n",
    "\n",
    "- Instead of calculating the pdf of the Gaussians, we work with the `log` of the pdf and use `tf.reduce_logsumexp`. This is mainly for numerical stability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPflow insides\n",
    "\n",
    "- Notice that we store vanilla numpy arrays in `Parameter` and `DataHolder` objects. The `@params_as_tensors` decorator will make sure that these variables are transformed into TensorFlow tensors once we are inside the decorated method.\n",
    "- The `@autoflow` decorator specifies the type and shape of a method's input variables. It will make sure that the graph, constructed inside the decorated method, will be executed inside a TF session and that the output is returned as a `np.array` when the method is called. This decorator allows one to execute TF graphs without having to worry about managing any `tf.session` objects, nor creating any placeholders or other TF related objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiments\n",
    "\n",
    "Let's see how our model works in practice. We will start with modeling the sinusoidal dataset we showed earlier. We do this by initializing a new instance of our MDN model. We further specify the dataset $(X, Y)$, the number of hidden units of the MDN's neural net and the number of mixture components $M$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MDN(X, Y, inner_dims=[100, 100], num_mixtures=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MDN instances are aware of their objective function. We can therefore directly start with minimizing the model. GPflow will make sure that only the variables stored as `Parameters` will be optimized. For the MDN the only parameters are the weights and the biases of the neural net, we stored them in `ParamList`s `self.Ws` and `self.bs`.\n",
    "\n",
    "We are using the `ScipyOptimizer`, which is a wrapper around scipy's L-BFGS optimization algorithm. GPflow, however, supports all other TensorFlow optimizers, such as `Adam`, `Adagrad`, `Adadelta`, ... as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: b'STOP: TOTAL NO. of ITERATIONS REACHED LIMIT'\n",
      "  Objective function value: -38.581859\n",
      "  Number of iterations: 1500\n",
      "  Number of functions evaluations: 1706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: b'STOP: TOTAL NO. of ITERATIONS REACHED LIMIT'\n",
      "  Objective function value: -38.581859\n",
      "  Number of iterations: 1500\n",
      "  Number of functions evaluations: 1706\n"
     ]
    }
   ],
   "source": [
    "from gpflow.test_util import notebook_niter\n",
    "ScipyOptimizer().minimize(model, maxiter=notebook_niter(1500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the validity of our model we will draw the posterior density. We also plot $\\mu(x)$ of the optimized neural net. Remember that for every $x$ the neural net will output, among other, $M$ means $\\mu_m(x)$. They determine the location of the Gaussians. We plot all $M$ of those means and use their corresponding mixture weight $\\pi_m(X)$ to determine their size. Larger dots will have more impact in the Gaussian ensemble. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mdn_plotting import plot\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12,6))\n",
    "for a in axes:\n",
    "    a.set_xlim(-4, 4)\n",
    "    a.set_ylim(-3, 3)\n",
    "plot(model, X, Y, axes, cmap=CMAP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets try another dataset\n",
    "\n",
    "This dataset is known as the two half moon dataset. It is available in the scikit-learn package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "def moon_data(N, noise):\n",
    "    data, _ = make_moons(n_samples=N, shuffle=True, noise=noise)\n",
    "    X, Y = data[:, 0].reshape(-1, 1), data[:, 1].reshape(-1, 1)\n",
    "    return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = moon_data(N, NOISE_STD)\n",
    "plt.plot(X, Y, 'ro', alpha=0.3);\n",
    "plt.xlabel(\"$x$\");\n",
    "plt.ylabel(\"$y$\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference in the MDN's setup is that we lower the number of mixture components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MDN(X, Y, inner_dims=[100, 100], num_mixtures=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
      "  Objective function value: -205.818872\n",
      "  Number of iterations: 4208\n",
      "  Number of functions evaluations: 4659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
      "  Objective function value: -205.818872\n",
      "  Number of iterations: 4208\n",
      "  Number of functions evaluations: 4659\n"
     ]
    }
   ],
   "source": [
    "ScipyOptimizer().minimize(model, maxiter=notebook_niter(10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vincent/pio/GPflow/doc/source/notebooks/manual/tailor/mdn_plotting.py:28: RuntimeWarning: divide by zero encountered in log\n",
      "  axes[0].contourf(plot_x, plot_y, np.log(probs), 500, cmap=cmap, vmin=-5, vmax=5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12,6))\n",
    "for a in axes:\n",
    "    a.set_xlim(-2, 3)\n",
    "    a.set_ylim(-1.5, 2)\n",
    "plot(model, X, Y, axes, cmap=CMAP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1] Bishop, Christopher M. Mixture density networks. Technical Report NCRG/4288, Aston University, Birmingham, UK, 1994.\n",
    "\n",
    "[2] Dutordoir, Vincent, et al. \"Gaussian Process Conditional Density Estimation.\" Advances in Neural Information Processing Systems. 2018."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
